{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grids in PorePy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we investigate the PorePy grids, and explain how to access information stored in the grid. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic grid construction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest grids are Cartesian. PorePy can create Cartesian grids in 1d, 2d, and 3d. \n",
    "There are 0d point grids as well, which are mainly used in the context of intersecting fractures. \n",
    "A 2d Cartesian grid can be created as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import porepy as pp\n",
    "\n",
    "nx = np.array([3, 2])\n",
    "g = pp.CartGrid(nx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting cells will be of unit size, thus the grid covers the domain $[0, 3]\\times [0,2]$. \n",
    "To specify the domain size, we need to pass a second argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "phys_dims = np.array([10, 10])\n",
    "g_2 = pp.CartGrid(nx, phys_dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once instantiated, the grids have both node coordinates and topological information that we can access and work with. \n",
    "The former will be presented below, and the latter is shown in a separate tutorial. \n",
    "\n",
    "To check the grid size, several attributes are provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "17\n",
      "12\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# Number of cells,\n",
    "print(g.num_cells)\n",
    "\n",
    "# number of faces,\n",
    "print(g.num_faces)\n",
    "\n",
    "# and number of nodes\n",
    "print(g.num_nodes)\n",
    "\n",
    "# The grid's dimension is obtained as follows\n",
    "print(g.dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also print a summary of the grid's properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cartesian grid in 2 dimensions.\n",
      "Number of cells 6\n",
      "Number of faces 17\n",
      "Number of nodes 12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The node coordinates are stored as "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 2., 3., 0., 1., 2., 3., 0., 1., 2., 3.],\n",
       "       [0., 0., 0., 0., 1., 1., 1., 1., 2., 2., 2., 2.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  3.33333333,  6.66666667, 10.        ,  0.        ,\n",
       "         3.33333333,  6.66666667, 10.        ,  0.        ,  3.33333333,\n",
       "         6.66666667, 10.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  5.        ,\n",
       "         5.        ,  5.        ,  5.        , 10.        , 10.        ,\n",
       "        10.        , 10.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_2.nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the second grid covers a larger area.\n",
    "\n",
    "We also see that even though the grids are 2d, the nodes have three coordinates. \n",
    "This is general, all geometric quantities in `PorePy` have three dimensions, even if they represent objects that are genuinely lower-dimensional. \n",
    "The reason is that for fractured media, we will often work with grids on fracture surfaces that are embedded in 3d domains, and treating this as special cases throughout the code turned out to be overly cumbersome. \n",
    "Also note that the third dimension was introduced automatically, so users need not worry about this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geometric quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compute additional geometric quantities, grids come with the method `compute_geometry()` that will add attributes `cell_centers`, `face_centers` and `face_normals`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.5 1.5 2.5 0.5 1.5 2.5]\n",
      " [0.5 0.5 0.5 1.5 1.5 1.5]\n",
      " [0.  0.  0.  0.  0.  0. ]]\n"
     ]
    }
   ],
   "source": [
    "g.compute_geometry()\n",
    "print(g.cell_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And similar for face information. \n",
    "It is of course possible to set the geometric quantities manually. \n",
    "Be aware that a subsequent call to `compute_geometry()` will overwrite this information.\n",
    "\n",
    "It may, for some users, be useful to consider grids with a Cartesian topology, but with perturbed geometry. \n",
    "This is achieved by perturbing the nodes, and then (re)-computing geometry:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.66666667 5.         8.33333333 1.66666667 5.         8.33333333]\n",
      " [2.5        2.5        2.5        7.5        7.5        7.5       ]\n",
      " [0.         0.         0.         0.         0.         0.        ]]\n",
      "[[2.28377751 5.36476949 8.74097087 2.17476519 5.23750014 8.71705232]\n",
      " [3.31083195 3.09688089 3.02494394 8.11590968 8.06407894 7.87505996]\n",
      " [0.         0.         0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "g_2.compute_geometry()\n",
    "print(g_2.cell_centers)\n",
    "g_2.nodes[:2] = g_2.nodes[:2] + np.random.random((g_2.nodes[:2].shape))\n",
    "g_2.compute_geometry()\n",
    "print(g_2.cell_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When perturbing nodes, make sure to limit the distortion so that the grid topology still is valid; if not, all kinds of problems may arise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`PorePy` provides two ways of visualizing the grid, matplotlib, and vtk/ParaView. Matplotlib visualization is done by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.plot_grid(g, plot_2d=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The matplotlib interface is most useful for quick visualization, e.g. during debugging, and other visualization tools are recommended to be used otherwise. \n",
    "\n",
    "For showing information about cell numbers, node numbers, face numbers and face normals, we can assign values to the parameters `info` and `alpha` in `pp.plot_grid()`.\n",
    "The parameter `info` decides which extra information is shown and the value of `alpha` determines the transparency of the grid.\n",
    "\n",
    "\n",
    "For instance, the lines of code below will plot a grid where we can see cell-, face- and node numbers, in addition to having all the cells colored with separate colors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell_id = np.arange(g.num_cells)\n",
    "pp.plot_grid(\n",
    "    g,\n",
    "    cell_value=cell_id,\n",
    "    info=\"cfn\",\n",
    "    alpha=0.5,\n",
    "    figsize=(10, 8),\n",
    "    plot_2d=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further information, see the documentation of `plot_grid`.\n",
    "\n",
    "The second visualization option dumps the grid to a vtu file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = pp.Exporter(g, \"grid\")\n",
    "e.write_vtu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file can then be accessed by e.g. [ParaView](https://www.paraview.org/). \n",
    "There is an entire tutorial related to exporting of data, so for a thorough walk-through of this we refer to the [exporter tutorial](./exporter.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simplex grids\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to constructor methods for Cartesian grids in 2d and 3d, PorePy supoorts simplex grids in both 2d and 3d. \n",
    "The simplex grids can be specified either by point coordinates and a cell-node map (e.g. a Delaunay triangulation), or simply by the node coordinates. \n",
    "In the latter case, the Delaunay triangulation (or the 3d equivalent) will be used to construct the grid. \n",
    "The representation of grid geometry and topology is the same for Cartesian and simplex grids. \n",
    "This allows for setting up simulations, defining discretization methods etc., in ways that are agnostic to the problem dimension.\n",
    "\n",
    "As an example, we make a triangle grid using the nodes of g, distorting the y coordinate of the two central nodes slightly: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nodes = g.nodes[:2]\n",
    "nodes[1, 5:7] = np.array([1.2, 0.8])\n",
    "g = pp.TriangleGrid(nodes)\n",
    "g.compute_geometry()\n",
    "pp.plot_grid(g, plot_2d=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A structured triangular grid (squares divided into two) is also provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = pp.StructuredTriangleGrid(np.array([3, 4]))\n",
    "g.compute_geometry()\n",
    "pp.plot_grid(\n",
    "    g,\n",
    "    plot_2d=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import of grids from external meshing tools\n",
    "\n",
    "Currently, PorePy supports import of grids from Gmsh. \n",
    "This is mostly used for fractured domains as described in the [mixed-dimensional grid tutorial](./mixed_dimensional_grids.ipynb).\n",
    "\n",
    "The grid structure in PorePy is fairly general, and can support a much wider class of grids than those currently implemented. \n",
    "To import a new type of grid, all that is needed is to construct the face-node and cell-face maps, together with importing the node coordinates. \n",
    "Remaining geometric attributes can then be calculated by the `compute_geometry()` function. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# What have we explored\n",
    "We have seen that PorePy provides convenient construction of Cartesian and simplex grids in 2d and 3d. The geometric information relevant to a grid (node coordinates, cell and face centers, cell volumes, face areas and face normal vectors) are computed by the method `compute_geometry()`. All coordinates are three-dimensional fields, even if the grid is of a lower dimension. Small grids can be visualized by the method `pp.plot_grid()`; for larger grids, it is advisable to export the grid to ParaView as demonstrated in the [exporter tutorial](./exporter.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
